This code should take all of the answers and store them in a big list.

answers_list = list()
index = 0
for(this_prior in c(TRUE, FALSE)){
  for(this_diffuseness in c(1, 2, 4, 8)){
    file_name = paste0("answers_informative=", this_prior, "_diffuseness=", this_diffuseness,".RData")
    load(file_name)
    answers_list[[file_name]] = answers$coda_answers
  }
}

From this list, we’re interested in estimate bias, ESS and \(\hat{r}\) across conditions. This next code chunk will return the 5 worst performing parameters for each condition.

get_statistics = function(list){
  abs_bias = abs(list$true_values[1:27] - list$mean[1:27])
  ess = list$ESS[1:27]
  rhat = list$RHAT[1:27]
  
  names(abs_bias) = rownames(list[1:27,])
  names(ess) = rownames(list[1:27,])
  names(rhat) = rownames(list[1:27,])
  
  return(list(absolute_bias = sort(abs_bias, decreasing = T)[1:5],
              expected_sample_size = sort(ess)[1:5],
              r_hat = sort(rhat, decreasing = T)[1:5]))
}
lapply(answers_list, get_statistics)
## $`answers_informative=TRUE_diffuseness=1.RData`
## $`answers_informative=TRUE_diffuseness=1.RData`$absolute_bias
##  direct_effect[1,3] M_fixed_effect[1,1] M_fixed_effect[1,5]  direct_effect[1,1] 
##              0.7724              0.4122              0.3634              0.3121 
## X_fixed_effect[4,1] 
##              0.2901 
## 
## $`answers_informative=TRUE_diffuseness=1.RData`$expected_sample_size
##  direct_effect[1,3] M_fixed_effect[1,1] M_fixed_effect[1,4]  direct_effect[1,1] 
##                 282                 283                1020                1027 
## M_fixed_effect[1,2] 
##                1102 
## 
## $`answers_informative=TRUE_diffuseness=1.RData`$r_hat
## M_fixed_effect[1,4]  direct_effect[1,1]  direct_effect[1,2] X_fixed_effect[5,2] 
##              1.0036              1.0024              1.0019              1.0014 
## M_fixed_effect[1,1] 
##              1.0013 
## 
## 
## $`answers_informative=TRUE_diffuseness=2.RData`
## $`answers_informative=TRUE_diffuseness=2.RData`$absolute_bias
##  direct_effect[1,3] M_fixed_effect[1,1] M_fixed_effect[1,5]  direct_effect[1,1] 
##              0.7740              0.4117              0.3325              0.3050 
## X_fixed_effect[4,1] 
##              0.2897 
## 
## $`answers_informative=TRUE_diffuseness=2.RData`$expected_sample_size
##  direct_effect[1,3] M_fixed_effect[1,1] M_fixed_effect[1,4] M_fixed_effect[1,3] 
##                 234                 235                 942                 946 
##  direct_effect[1,1] 
##                 971 
## 
## $`answers_informative=TRUE_diffuseness=2.RData`$r_hat
##  direct_effect[1,3] M_fixed_effect[1,1]  direct_effect[1,1]  direct_effect[1,2] 
##              1.0096              1.0094              1.0046              1.0026 
## M_fixed_effect[1,6] 
##              1.0015 
## 
## 
## $`answers_informative=TRUE_diffuseness=4.RData`
## $`answers_informative=TRUE_diffuseness=4.RData`$absolute_bias
##  direct_effect[1,3] M_fixed_effect[1,1] M_fixed_effect[1,5] M_fixed_effect[1,6] 
##              2.9352              1.4661              0.5405              0.4164 
## X_fixed_effect[4,1] 
##              0.2954 
## 
## $`answers_informative=TRUE_diffuseness=4.RData`$expected_sample_size
## X_fixed_effect[1,3] X_fixed_effect[2,3] X_fixed_effect[2,2] X_fixed_effect[1,2] 
##                  13                  21                  45                  53 
## M_fixed_effect[1,5] 
##                  55 
## 
## $`answers_informative=TRUE_diffuseness=4.RData`$r_hat
## X_fixed_effect[1,3] X_fixed_effect[2,3] M_fixed_effect[1,2] X_fixed_effect[2,2] 
##              3.1295              2.7646              2.2885              2.2016 
## M_fixed_effect[1,5] 
##              2.0519 
## 
## 
## $`answers_informative=TRUE_diffuseness=8.RData`
## $`answers_informative=TRUE_diffuseness=8.RData`$absolute_bias
##  direct_effect[1,3] X_fixed_effect[1,2] M_fixed_effect[1,1] X_fixed_effect[2,1] 
##              3.8901              2.9844              1.8617              1.1206 
## X_fixed_effect[3,3] 
##              1.1126 
## 
## $`answers_informative=TRUE_diffuseness=8.RData`$expected_sample_size
## X_fixed_effect[1,2] X_fixed_effect[5,3] X_fixed_effect[6,3] X_fixed_effect[2,1] 
##                   1                   1                   2                   3 
## X_fixed_effect[2,3] 
##                   3 
## 
## $`answers_informative=TRUE_diffuseness=8.RData`$r_hat
## X_fixed_effect[5,3] X_fixed_effect[1,2] X_fixed_effect[6,3] X_fixed_effect[2,3] 
##             20.2772             17.7096             16.3637             11.3524 
## X_fixed_effect[2,1] 
##             11.0613 
## 
## 
## $`answers_informative=FALSE_diffuseness=1.RData`
## $`answers_informative=FALSE_diffuseness=1.RData`$absolute_bias
##  direct_effect[1,3] M_fixed_effect[1,1]  direct_effect[1,1] X_fixed_effect[4,1] 
##              1.1790              0.6038              0.3471              0.2893 
## X_fixed_effect[1,1] 
##              0.1057 
## 
## $`answers_informative=FALSE_diffuseness=1.RData`$expected_sample_size
## M_fixed_effect[1,1]  direct_effect[1,3]  direct_effect[1,1] M_fixed_effect[1,3] 
##                 258                 264                 844                 927 
## M_fixed_effect[1,4] 
##                 968 
## 
## $`answers_informative=FALSE_diffuseness=1.RData`$r_hat
##  direct_effect[1,2] M_fixed_effect[1,5]  direct_effect[1,1] M_fixed_effect[1,1] 
##              1.0027              1.0023              1.0020              1.0013 
##  direct_effect[1,3] 
##              1.0013 
## 
## 
## $`answers_informative=FALSE_diffuseness=2.RData`
## $`answers_informative=FALSE_diffuseness=2.RData`$absolute_bias
##  direct_effect[1,3] M_fixed_effect[1,1]  direct_effect[1,1] X_fixed_effect[4,1] 
##              1.1489              0.5806              0.3058              0.2953 
## M_fixed_effect[1,5] 
##              0.1929 
## 
## $`answers_informative=FALSE_diffuseness=2.RData`$expected_sample_size
## X_fixed_effect[4,3] M_fixed_effect[1,1]  direct_effect[1,3] X_fixed_effect[4,2] 
##                 107                 166                 177                 242 
## M_fixed_effect[1,4] 
##                 403 
## 
## $`answers_informative=FALSE_diffuseness=2.RData`$r_hat
## X_fixed_effect[4,3] X_fixed_effect[4,2] M_fixed_effect[1,4] X_fixed_effect[3,3] 
##              1.3768              1.1906              1.1725              1.0996 
##  direct_effect[1,1] 
##              1.0929 
## 
## 
## $`answers_informative=FALSE_diffuseness=4.RData`
## $`answers_informative=FALSE_diffuseness=4.RData`$absolute_bias
## M_fixed_effect[1,2] M_fixed_effect[1,3]  direct_effect[1,3] M_fixed_effect[1,4] 
##              0.7392              0.5406              0.5386              0.4916 
## M_fixed_effect[1,6] 
##              0.4880 
## 
## $`answers_informative=FALSE_diffuseness=4.RData`$expected_sample_size
## M_fixed_effect[1,2] X_fixed_effect[2,2] M_fixed_effect[1,1]  direct_effect[1,3] 
##                  25                  44                  54                  58 
## M_fixed_effect[1,3] 
##                  61 
## 
## $`answers_informative=FALSE_diffuseness=4.RData`$r_hat
## M_fixed_effect[1,2] X_fixed_effect[2,2] M_fixed_effect[1,1]  direct_effect[1,3] 
##              3.2397              2.2387              1.9778              1.8894 
## M_fixed_effect[1,4] 
##              1.7316 
## 
## 
## $`answers_informative=FALSE_diffuseness=8.RData`
## $`answers_informative=FALSE_diffuseness=8.RData`$absolute_bias
##  direct_effect[1,3] M_fixed_effect[1,1] X_fixed_effect[1,2] X_fixed_effect[5,3] 
##              5.6021              2.8615              2.5455              1.3166 
## X_fixed_effect[1,1] 
##              1.1005 
## 
## $`answers_informative=FALSE_diffuseness=8.RData`$expected_sample_size
## X_fixed_effect[5,3] X_fixed_effect[1,2] X_fixed_effect[2,1] X_fixed_effect[1,3] 
##                   1                   2                   3                   3 
## X_fixed_effect[3,3] 
##                   3 
## 
## $`answers_informative=FALSE_diffuseness=8.RData`$r_hat
## X_fixed_effect[5,3] X_fixed_effect[3,3] X_fixed_effect[1,2] X_fixed_effect[1,3] 
##             15.5195              7.3310              6.7433              5.5906 
## X_fixed_effect[2,1] 
##              5.1513

It seems the less diffuse options work much better than the more diffuse options. Lets go with uninformative and diffuseness = 1

Let’s see a plot of the chains for the conditions with mean 0 priors and less diffuseness

library(rjags)
## Loading required package: coda
## Linked to JAGS 4.3.1
## Loaded modules: basemod,bugs
load("answers_informative=FALSE_diffuseness=1.RData")
plot(answers$codaSamles) #there's a typo coded in my entry here, but the code should still work as expected